Survival Analysis and the Cox Proportional Hazards Model: A Beginner-Friendly Guide
From Kaplan-Meier curves to hazard ratios with runnable Python Code throughout
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Bug Detection Blind Spots in AI Coding Harnesses (GStack and Beyond)
28 debugging experiments reveal that AI struggles less with complexity than with missing information.
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Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call)
LoRA fine-tuning solved our under-labeling problem. Whether it makes sense for you depends on three questions.
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Retrieve One Row from a Table, Not the Whole Table: Row-Level Chunks for RAG
Enterprise Document Intelligence [Vol.1 #7sexies] - The unit of retrieval doesn’t have to be a page or a paragraph. When the corpus carries tables, each body row with its column headers is a chunk in its own right, and it’s often the one row the reader asked about
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The Types of Dimensions in a Star Schema, and How to Use Them
Dimensions are one of the two main object types in dimensional modelling. But what are the different types of dimensions? And how can you use them?
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Bayesian Guardrails for AI Decisions: Measuring Uncertainty Before Automating Decisions
AI systems should not automate a decision simply because they can provide a prediction. A decision system should consider how uncertain the prediction is and defer if a mistake would be costly.
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How Benders Decomposition Works, Part II: Feasibility Cuts
Learning about Farkas' lemma and how it can inform Benders decomposition to learn from infeasibility, applied to the capacitated facility location problem.
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How to Scale an Integration Pipeline Without Breaking Correctness
A production account of scaling an enterprise integration pipeline from 500 to 8,000 events per second, and the two correctness guarantees the throughput work was never allowed to trade away.
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Kimi K3’s 1M Token Context Window vs. RAG: Cost, Latency and Answer Quality
A controlled comparison of a top-5 RAG pipeline and a full 127,000 token prompt on the same 12 questions, same system prompt and same model. Graded blind on correctness, completeness and grounding.
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People can accept tradeoffs when they see value — but if they don’t, what happens?
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Webwright: Why AI Web Agents Should Write Code, Not Click
For years, web agents have worked one click at a time—and often fallen apart on long tasks. Microsoft Research’s Webwright makes a different bet: give the model a terminal and let it write the program instead. On long-horizon tasks, the same GPT-5.4 model jumps from 33.5% to 60.1% success. And inste...
Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them
How autonomous agents broke two decades of capacity planning — and what to build instead
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How to Perform Effective Project Management with AI
Become a more productive software engineer with LLMs
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Running SQL Concurrently Across Three Remote DuckDB Servers with Quack
A small experiment in remote SQL execution
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How to Shine as a Data Scientist in the Vibe Coding Era
Here's how to be the Data Scientist who thrives in a world where coding is a commodity.
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Becoming Human AI Is Expanding — Here’s What’s Changing
For years, this has been where you’ve found us — through Medium, whenever we published something worth your time.That’s changing a little, starting now.We’re expanding into a proper newsletter, so expect to hear from us more regularly than “whenever a new post goes up.” If you’ve got email notificat...
How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs
Google's Open Knowledge Format (OKF) is a Markdown+YAML skeleton for sharing knowledge between humans and AI agents. This post reuses that skeleton for a very specific job — an agent-to-agent hand-off of pre-tokenized integer arrays between three Qwen2.5-Coder models (7B, 3B, 1.5B) — and shows the 2...
LangChain vs LangGraph: 4 Key Differences and When to Use Each
A practical guide to choose the proper tool for your agentic workflows and systems
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In simple terms, retrieval-augmented fine-tuning, or RAFT, is an advanced AI technique in which retrieval-augmented generation is joined with fine-tuning to enhance generative responses from a large language model for specific applications in that particular domain.It allows the large language model...